Grant Elliott

The AI Productivity Paradox: personal efficiency doesn't automatically become team efficiency. Most organizations jumping into AI adoption make the same mistake: they measure what's easy rather than what's meaningful. Someone saves an hour using ChatGPT to draft a report. A developer uses Copilot to write code faster. These feel like wins, and they might be, but without a structured way to understand who  benefits, how much , and whether that benefit compounds , you're essentially flying...

We are currently living through what I believe is “the age of AI innocence.” By that, I mean a period in which we still believe these systems are fundamentally designed to serve the needs of the end user. When an answer is wrong, we interpret it as a limitation of probability or context rather than a reflection of incentive. We assume the model is trying to help, and we expect it to improve as training, context, and compute advance. In many ways, it will. But this assumption of innocence...

One of the questions I regularly get asked when I demo our new AI platform (beta launch coming soon) is whether we have developed our own AI learning model. When I explain that we have not, there is usually a short pause. The implication is clear. If you are serious about AI, surely you are training your own model. And if not, then what exactly makes your product defensible? It is a fair question given where we are in the AI hype cycle. Right now, building and owning your own AI model feels...